Intruder detection using a wireless service mesh network

ABSTRACT

The disclosed teachings relate to intruder detection. Some of the subject matter described herein includes a computer-implemented method for detecting physical movement using a wireless mesh network that provides wireless data communication, the wireless mesh network having a plurality of mesh points, each mesh point having a wireless coverage, the method including compiling a database of known devices based on monitoring unique identifiers of known devices that have previously conducted communication with the wireless mesh network through the plurality of mesh points; upon detecting a physical presence of a subject device within a physical space of the wireless mesh network, determining, based on the database of known devices, whether the physical presence of the subject device belongs to an anomaly; and when the physical presence of the subject device is determined to be an anomaly, causing a security action to be performed.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation (CON) application of U.S. Utilitypatent application Ser. No. 15/465,405, filed on Mar. 21, 2017, entitled“INTRUDER DETECTION USING A WIRELESS SERVICE MESH NETWORK,” which claimspriority to U.S. Provisional Patent Application No. 62/406,325, filed onOct. 10, 2016, entitled “DISTRIBUTED MULTI-BAND WIRELESS NETWORKINGSYSTEM,” which is hereby incorporated by reference in its entirety.

TECHNICAL FIELD

The disclosed teachings relate to intruder detection using a wirelessnetwork, and more particularly to using wireless technology to detectintruders in physical spaces such as rooms, buildings or other areas.

BACKGROUND

Home intrusions are a major concern for many home owners and renters.Some security monitoring systems for detecting home intrusions requirepurchasing security system equipment that can be expensive to install.For example, security system equipment can include contacts that can beinstalled on windows and doors. These contacts can be used to determinewhether the windows and doors are being opened. However, each window anddoor in the home needs to have a contact, and each of the contacts mightrequire wiring to a control system of the security monitoring system.This can make installation difficult. In another example, motion controlsensors can be installed in rooms of the home to detect unexpectedmovement. However, many of these motion control sensors might need to beinstalled high on a wall or close to the ceiling, also resulting in adifficult installation.

By contrast, wireless mesh networks can be easily set up in the home.For example, multiple devices can be grouped together to implement awireless network (e.g., conforming to one of the Institute of Electricaland Electronics Engineers (IEEE) 802.11 standards implementing awireless local area network (WLAN)). The multiple devices can be placedin different locations of the home to provide better network coveragethan a single device (e.g., a single device providing a router and/oraccess point). For example, the devices can be merely placed upontables, shelves, desks, or other furniture.

SUMMARY

Some of the subject matter described herein includes acomputer-implemented method for detecting physical movement using awireless mesh network that provides wireless data communication, thewireless mesh network having a plurality of mesh points, each mesh pointhaving a wireless coverage, the method including compiling a database ofknown devices based on monitoring unique identifiers (UIDs) of knowndevices that have previously conducted communication with the wirelessmesh network through the plurality of mesh points; upon detecting aphysical presence of a subject device within a physical space of thewireless mesh network, determining, based on the database of knowndevices, whether the physical presence of the subject device belongs toan anomaly; and when the physical presence of the subject device isdetermined to be an anomaly, causing a security action to be performed.

In some implementations, the database of known devices includes UIDs ofknown devices, and pattern of known devices.

In some implementations, the pattern of the known devices includes timeof presence and estimated locations of the known devices within thewireless mesh network.

In some implementations, the physical presence of the subject device isdetermined to be an anomaly when data in the database of known devicesshow that an estimated location of the subject device deviates from thepattern of known devices.

In some implementations, the physical presence of the subject device isdetermined to be an anomaly when a location of the subject device iswithin a determined boundary.

In some implementations, the determined boundary is determined by thepattern of known devices or by an administrator of the wireless meshnetwork.

In some implementations, the physical presence of the subject device isdetermined to be an anomaly when the historic data show that a timing ofthe subject device's physical presence deviates from the pattern ofknown devices.

In some implementations, the physical presence of the subject device isdetermined to be an anomaly when the subject device has a UID not in thedatabase of known devices.

In some implementations, determining whether the physical presence ofthe subject device is to be an anomaly is based on a moving window ofdata samples relating to the physical presence of the subject deviceover a predetermined period of time.

In some implementations, the physical presence of the subject device isan anomaly if an average value of the moving window varies more than athreshold.

In some implementations, the physical presence of the subject device isdetected based on wireless communication prior to association betweenthe wireless mesh network and the subject device.

In some implementations, the wireless communication prior to associationcontains a UID of the subject device.

In some implementations, the physical presence of the subject device isdetected based on signal interference caused by the subject device or bythe human body of an intruder with physical possession of the subjectdevice.

In some implementations, the method includes causing a number of meshpoints in the wireless mesh network to estimate a location of thesubject device based on a proximity between each of the number of meshpoints and the subject device.

In some implementations, the method includes determining the proximitybetween a respective mesh point and the subject device based on one ormore of: (1) a round trip time (RTT) in communications between arespective mesh point and the subject device, (2) a time of arrival(TOA) of communications from the subject device to the respective meshpoint, or (3) a received signal strength indicator (RSSI) value ofcommunications from the subject device to the respective mesh point.

In some implementations, the method includes determining the proximitybetween a respective mesh point and the subject device based on whetherthe subject device is able to successfully communicate with one or moreradio modules on the respective mesh point.

In some implementations, the one or more radio modules have different ortunable wireless communication ranges.

In some implementations, the method includes training one or moremachine learning models on the known devices during an initial trainingperiod.

In some implementations, whether the physical presence of the subjectdevice belongs to an anomaly is determined based on the one or moretrained machine learning models.

In some implementations, training the one or more machine learningmodels comprises establishing a hidden Markov model to model movement ofthe subject device for determining the anomaly.

In some implementations, the method includes causing, by using abackhaul communication mechanism that is not client-serving, a number ofmesh points in the wireless mesh network to estimate a location of thesubject device.

In some implementations, the method includes instructing, by using abackhaul communication mechanism that is not client-serving, a number ofmesh points in the wireless mesh network to utilize one or moreclient-serving radio modules that have different or tunable wirelesscommunication ranges for estimating a location of the subject device.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a process for storing activity.

FIG. 2 illustrates an example top view of an access point (AP) unitdeployed in a home setting.

FIG. 3 illustrates an example of intruder detection using a wirelessmesh network.

FIG. 4 illustrates an example of an administrative console.

FIG. 5 illustrates an example of communication between Generic AttributeProfile (GATT) servers and clients.

FIG. 6 illustrates an example of a detected intruder device.

FIG. 7 illustrates an example of a use of a Hidden Markov Model (HMM) tomodel movement of devices and/or masses.

FIG. 8 illustrates an example of a use of the HMM for coordination andtime measurements.

FIG. 9 illustrates an example of intruder devices that are between unitsof a wireless mesh network.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forthsuch as examples of specific components, circuits, and processes toprovide a thorough understanding of the present disclosure. Also, in thefollowing description and for purposes of explanation, specificnomenclature is set forth to provide a thorough understanding of thepresent embodiments. However, it will be apparent to one skilled in theart that these specific details may not be required to practice thepresent embodiments. In other instances, well-known circuits and devicesare shown in block diagram form to avoid obscuring the presentdisclosure.

The term “coupled” as used herein means connected directly to orconnected through one or more intervening components or circuits. Any ofthe signals provided over various buses described herein may betime-multiplexed with other signals and provided over one or more commonbuses. Additionally, the interconnection between circuit elements orsoftware blocks may be shown as buses or as single signal lines. Each ofthe buses may alternatively be a single signal line, and each of thesingle signal lines may alternatively be buses, and a single line or busmight represent any one or more of a myriad of physical or logicalmechanisms for communication (e.g., a network) between components. Thepresent embodiments are not to be construed as limited to specificexamples described herein but rather to include within their scope allembodiments defined by the appended claims.

This disclosure describes devices and techniques for detecting anintruder within a physical space using a wireless mesh network. In oneexample, a wireless mesh network can be implemented by a systemincluding access points (APs), or satellite nodes, placed within aphysical space such as a home. An example of such a wireless meshnetwork is described in U.S. patent application Ser. No. 15/287,678 tothe same applicant herein; the content of which is hereby incorporatedby reference in its entirety.

When a device (e.g., a mobile phone) scans, or probes, for availablewireless networks within range of the coverage of the wireless meshnetwork or attempts to connect with the wireless mesh network, this canexpose a unique identifier (UID) of the device, for example, a mediaaccess control (MAC) address assigned to the device's network interfacecontroller (NIC). Other examples of a unique identifier can include auniversally unique identifier (UUID), AID, etc. The system can recordthe unique identifiers of several devices scanning or connecting withthe wireless mesh network to generate a database of known, orrecognized, devices. Later, when another device is within range of thewireless mesh network (e.g., scanning or connecting with the wirelessmesh network), the database of known devices can be used to determinewhether it is recognized as a known device. If not, this can representan intrusion into the physical space housing the APs, and therefore, asecurity action can be performed. For example, the homeowner can bealerted via email, text message, etc. that an intrusion into thephysical space has been detected using the wireless mesh network.

Additionally, characteristics of the signals between the unrecognizeddevice and one or more of the APs can be used to determine whether thereis an intruder in the physical space. For example, the time of theestablishment of the communication connection providing the signal, theround trip time (RTT) of data between an AP and the device, receivesignal strength indication (RSSI) of the communication connection, etc.can also be used to determine an intrusion into the physical space.Thus, a security system can be provided by a wireless mesh network thatcan be easily installed. Moreover, the cost of the security system canbe reduced because the existing components of the wireless mesh networkcan be used to detect an intruder.

In more detail, network security is important. Nowadays almost everybodyhas a phone with them when they move. The phone may be used to detect anintruder on a wireless network, and therefore determine that an intruderhas violated (e.g., trespassed into) a physical space such as a home inwhich the wireless network is deployed. Particularly, intruder detectioncan be performed by monitoring Wi-Fi and Bluetooth activities using oneor more access point (AP) units. The system may monitor probe requeststo detect the presence of a new device. If a certain receive signalstrength indicator (RSSI) pattern is seen for a unique identifier (UID),such as a new MAC address, the information can be used for intruderdetection. Also, certain time of arrival or round trip delay on a newMAC address or AID may be used to detect the intruder. Additionally,indoor localization techniques can also be used to detect an intruder. AUID can include MAC Address, UUID, and/or AID. The UID can be providedin one or more packets received from the devices.

FIG. 1 illustrates an example of a process for storing activity. Atblock 101, activity within the surrounding environment of a wirelessnetwork is monitored. Monitoring can include detecting the presence ofdevices within the coverage of the wireless network, and/or determiningchanges in characteristics of signals, such as signal strength ofsignals providing communications between the devices and the APs of thewireless network (e.g., a wireless mesh network having multiple APs). Inan embodiment, the one or more AP units can allow the device to connectto the AP unit and the data packets can be monitored for suspiciousactivity. The AP units can be configured as a mesh network and can bemesh points. The signal strength can be measured over time to determinesignal patterns associated with dates and/or times. At block 102, thiscollected data can be stored. For example, the collected data can bestored in a database accessible by the security system. In someembodiments of block 102, results of monitoring for the presence of userdevices in the environment can be stored with associated dates and/ortimes. In at least one embodiment, monitoring can include tracking anelectronic tag. For example, a pet collar can be tagged with anelectronic tag, the proximity of the pet to the device can be measured,and the owner can be alerted when the chip passes a specific distancethreshold. In some embodiments, the techniques discussed with referenceto blocks 101 and 102 can be performed during a “training” or“calibration” phase of the security system using the wireless meshnetwork. For example, the first few hours, days, or weeks can involverecording the data discussed herein to determine the expected activitywithin the environment of the physical space in which the wireless meshnetwork is deployed. This results in a database indicating devices thathave previously communicated with the wireless mesh network, andtherefore, a listing of known or recognized devices within the coverageof the wireless mesh network. Patterns of activities of those devices(e.g., location in the home, time, movements, etc.) can also bedetermined. This can also include storing data indicating variouscharacteristics of signals provided to and/or received from the devices,as discussed herein. As discussed later herein, after the trainingphase, intruders can be detected by comparing data related to currentlydetermined activities within the physical space with the data related tothe training phase. In some embodiments, multiple training phases can beperformed. That is, the data related to the expected activity can beupdated from time-to-time as discussed later herein.

FIG. 2 illustrates an example top view of an AP unit in a home setting.A signal is omitted from and/or collected at access point (AP) device201. In at least one embodiment, the AP unit is a transceiver. In atleast one embodiment, multiple AP units, one or more SLAVE units, and/orsensors are being used to collect and/or omit signals. The signalsomitted and/or collected can include one or more short rangecommunication technologies such as, Wi-Fi, INSTEON®, Infrared, WirelessUSB, Bluetooth®, Skybeam®, Z-Wave®, radio, ZigBee®, Body Area Network,and/or any available wireless technology. In at least one embodiment,the signal omitted and/or collect includes signals of different radiofrequencies and/or different ranges on the frequency.

In one example, a device can scan or probe for available wirelessnetworks. As previously discussed, this process can provide the uniqueidentifier of the device to the security system. In some embodiments,the unique identifier is provided before the association processinvolving the device and AP results in a grant of association in whichthe device is allowed to use the wireless mesh network (e.g., connectedto the Internet, etc.). Thus, devices can be detected before associationbetween the AP of the wireless mesh network and the device. The scanningor probing of the wireless mesh network can include a probe requestframe of a WLAN protocol, a GATT profile of a Bluetooth® protocol, etc.

Algorithms such as Hidden Markov Models (HMM) can be used to determinethe topology of the room using response times of signals between the APsand devices, time of arrival of signals between the APs and devices,round trip delay (RTD) of signals between the APs and devices, movementof devices between APs, and/or the RSSI of signals between the APs anddevices.

In at least one embodiment, the two or more AP units, SLAVE units and/orsensors can communicate via a backhaul and may have an independentwireless physical radio. The AP units, SLAVE units and/or sensors can beconfigured as a network mesh. In an embodiment, the backhaul can bereserved for AP units (mesh points) to communicate. For example, thebackhaul can be a communication link used to provide communicationsbetween different APs. Devices (e.g., mobile phones) connect with thewireless mesh network using a fronthaul communication linkcommunicatively coupling the device with an AP. The backhaul andfronthaul communication links can use different frequency bands. Thearrangement of backhaul communication links between different APs canrepresent at topology of the wireless mesh network. In some embodiments,the fronthaul communication links can be used to determine the presenceof an intruder, for example, the UID can be received by an AP on afronthaul communication link.

FIG. 3 illustrates an example of intruder detection using a wirelessmesh network. At block 301 home activity data is collected and stored asdescribed with reference to FIG. 1. The activity data is then used togenerate an activity profile at block 302. In an embodiment, theactivity profile can include devices in the home associated withspecific days, dates and times. For example, the activity profile caninclude four detected devices every Monday at 8 p.m. to 11 p.m., whichcorrelate to Monday night football gatherings. The activity profile cantherefore be used to determine that on Monday night 8 pm-11 pm unknowndevices will enter the home, and therefore, these should not bedetermined to be intruders. Machine learning can be used to determinethe difference between outliers and/or significant events. The fourunknown devices can be stored in association with the activity profiles.Devices and/or patterns can be stored in association with activityprofiles. Calendar entries can be used to assist the system withidentifying the activity. For example, multiple devices entering a homeonce per year can be determined to be insignificant by the machinelearning algorithm or can be determined to be a significant eventassociated with a holiday party. In an embodiment, external informationin conjunction with data can provide context to create an activityprofile. The external information can include calendars, emails, socialmedia information, and/or text messages. For example, the securitysystem employing the wireless mesh network for intruder detection candetermine that four new unique identifiers (e.g., MAC addresses)representing four unknown devices have probed or connected with thewireless mesh network. The security system can access a calendar (e.g.,an online calendar on a cloud service) and determine that the homeownerhas a record for a party that will be hosted on that day in a timeperiod in which the four new unique identifiers were detected. As aresult, the security system can refrain from sending an alert to thehomeowner because these are likely to be invited partygoers rather thanintruders.

In at least one embodiment, the determined location of the device can beincluded in activity data. The location can be determined using probeinformation such as which AP unit is being probed, and/or the round tripdelay (RTD) (e.g., the duration of time it takes for a signal to be sentby an AP and for an acknowledgement of that signal to be received by theAP from the device) and/or RSSI information. The movements derived fromthis activity can be analyzed to determine movement prints at blocks 302and/or 304. For example, changes in the RSSI values of signals receivedfrom the devices can be correlated with movement because as the devicemoves within the home, the path to the AP can change, resulting in adifferent RSSI at different times.

In some embodiments, patterns of these characteristics of the signals(e.g., RTD, or RSSI) can be used to establish a boundary of the physicalspace for which intruder detection can be implemented. This can beuseful because the boundary (or perimeter) of the wireless mesh network(e.g., the range or coverage of the wireless mesh network) might extendfarther than the physical space (e.g., the home). During the calibrationor training phase as previously discussed, the boundary of the physicalspace can also be established using machine learning. For example, asthe user walks around his or her home with a mobile device, the RSSI ofcommunication connections or signals between the mobile device and theAPs can be determined. This can establish a range of RSSI values thatcan be representative of the mobile device being within the physicalspace and, therefore, also within the range of coverage of the wirelessmesh network. An RSSI value outside of that range can be indicative of adevice that is within range of the wireless mesh network, but not withinthe physical space. Thus, if an RSSI value outside of that range isdetermined, then this can indicate that someone is outside of the homeand might not be an intruder. If the RSSI value is within that range,then this can indicate that someone is inside of the home and,therefore, a security action can be performed. If machine learningalgorithms are employed, then the boundary can be adjusted over time.For example, the boundary can be tuned, or adjusted, to be more accurateover time by collecting more data regarding RSSI values, using moredevices, gathering information from more locations, etc. In someembodiments, the boundary can be determined as set by the user. Forexample, during the training or calibration phase, the user can walkaround the physical space for which intruder detection should beimplemented. The RSSI values during this can be determined in a similarmanner to define the boundary.

In one embodiment, the RTD (or RTT) can be used to determine thedistance of the device from one or more of the APs. If the RTDdecreases, this can mean that the device is getting closer and if theRTD is within a threshold time range, then this can indicate that thedevice is within a portion of the physical space covered by the wirelessmesh network for which intruders should be detected. These techniquescan also be used to determine movement and if movement is detected whenno device should be moving (e.g., devices at 2 a.m. should be expectedto be resting in a single place while the homeowner is asleep), thenthis can indicate an intruder is in the home. In some embodiments, thesemeasurements can be averaged out with a moving window, or movingaverage, and if that value is within a threshold value range or outsideof the threshold value range, then this can indicate that the physicalpresence of that detected device is an anomaly (e.g., an intruder).

In at least one embodiment, abnormal movement patterns of multipledevices can be analyzed. Known user movements can be identified andassociated with activity rules. The system may determine the normalmovement of a known individual and when an unknown guest and/or deviceenters the home, the movement of the known individual can be analyzed todetermine if the movement of the known user changes. For example, duringa home invasion, the movement of the known user or users may changeimmediately after the unknown guest enters the home. In another example,an intruder can be detected by determining that a device with anunrecognized unique identifier has probed the wireless mesh network latein the evening. If at that time the homeowner has a device with a uniqueidentifier that is usually placed in a bedroom (e.g., on a nightstand)but it is now moving towards another room, then this might indicate thatthe homeowner is reacting to an intruder in the home. Thus, historicaldata representing how a device is usually operated within the physicalspace can be determined and if current activity associated with thatdevice deviates from the historical data upon detection of an unknowndevice in the physical space, various actions as disclosed herein can beperformed. The rule may trigger an action similar to the actionsdescribed in blocks 304 and 305 below.

In an embodiment, the activity can be collected at a specified intervalsuch as set amount of minutes, seconds, and/or hours. In someembodiments, the collected activity can be grouped to reduce storingunnecessary data and/or increase data processing times. Long periods oftime may pass during which the activity information will remainunchanged. For example, the workweek day activity in the home may remainthe same for 8 hours because no one is home. The identical and/or nearlyidentical information can be grouped to one activity record with theassociated time period.

In at least one embodiment, the activity profile can be manuallyconfigured by the user to identify the devices and/or individuals thatare expected in the home at the associated dates, days and/or times. Adevice associated with a cleaning person can be set to be expected inthe home on Friday mornings. As a result, when the cleaning personenters the home on Friday morning and an unknown device is recognized(e.g., the unique identifier is not recognized), a security alert can beavoided. This can reduce the number of false positives of intruderdetections. When the device information is not known, the activityprofile can be manually set up to expect unknown devices during specificdates, days and/or times.

The activity profile can include determining the movements and/or massesin the home by tracking the change of RSSI and/or RTD of signals betweendevices and APs. One or more machine learning algorithms may determinethe patterns of the collected data and build the activity profile. In anembodiment, a house guest with an uncharged device can be detected bythe movement in the home. The monitored activity data can include atimestamp. The time stamp associated with collected data can aid indetermining the number of individuals in the home. For example, thedetermination can be made using machine learning algorithms todistinguish whether the detection of a mass next to the couch is thesame mass that was detected by the dining room table two minutes prior.The activity information gathered from probing the guest device can beused in combination with the activity collected based patterns whichdetects masses to build activity profiles, trigger rules, and/oridentify appropriate actions. The system can recognize the pattern ofmovement of devices which may belong to an intruder. The movements canbe analyzed to determine movement prints. Different activity profiles,based on patterns, can be learned over different times of the day. Forexample, between the hours of 1:00 a.m. to 5:00 a.m., this pattern maybe different than 4:00 p.m. to 8:00 p.m.

The activity profiles can be built over time by collecting activityinformation. In an embodiment, an initial setup can be used to train themethod and/or system using collected activity of block 301. For example,as previously discussed, during the first few hours or few days, thesystem may be trained to determine the known device at home. The systemmay also learn the UIDs such as the MAC address of neighbor devices(e.g., devices in a neighboring house that is within the coverage of thewireless mesh network) and other devices that are regularly within therange of the coverage of the wireless mesh network. For example, devicesof delivery drivers dropping off packages at the door of the home can bedetermined and indicated as known devices. After the initialconfiguration the system may start sending alerts that an unknown deviceis identified and is deemed suspicious, representing a potentialintruder in the home. The home can include any environment within rangeor coverage of the wireless mesh network of the system.

At block 303 the data continues to monitor for activity includingchanges in signal RSSI and/or RTD, wireless connectivity information,probe information from wireless devices, and/or information fromelectronic tags. The activity data collected at block 303 maycontinuously be stored and used to update the activity profile asdescribed in FIG. 2 and blocks 301 and 302. Once the activity profile isbuilt it can constantly be updated with the most relevant informationand using machine learning algorithms.

In at least one embodiment at block 304, the identity of that guest canbe determined using historical data of its previous visits by comparingthe mass of the guest calculated using the signal RSSI and/or RTD to themass associated with previous guests having set devices. For example, acleaning person having a mass of 62 kilograms (kg) may enter a homewithout the mobile device and based on the historical information, thatperson's mass of approximately 60 kg allows for derivation of theprevious visit information and the devices previously associated withthat mass. The speed of movement can also be derived using the change inRSSI and/or RTD signal as the guest moves through the home. Similar tothe example above, the determined mass of the guest and the speed of themovement can be used to match historical data.

In an embodiment, the physical presence of a device within the home canbe detected based on signal interference caused by the device or bodycarrying the device. For example, the security system can measureinterference of signals provided to and/or from the devices.

The block at 304 can include determining abnormal behavior. Determiningabnormal behavior can include detecting the electronically tagged itemleaving the AP range. For example, according to the activity profile theelectronically tagged dog collar only leaves the AP range whenaccompanied by a mobile device or a mass over 50 kg. The comparison candetermine that an abnormal event is occurring when the electronicallytagged dog collar leaves the AP range unaccompanied. Additionally, theabnormal events of adults, children, and/or pets can be detected usingthe RSSI and/or RTD. The activity profile can ascertain that massesunder 30 kg do not leave the AP range unaccompanied without masses over50 kg and can determine that an abnormal event is occurring when theaccompanying condition fails to be met. In an embodiment, externalinformation in conjunction with data to provide context to determinewhether the behavior is abnormal. The external information can includecalendars, emails, social media information, and/or text messages.

In an embodiment, activity rules can be configured by the user. Forexample, although the system may not be able to determine that ahomecare attendant spending time next to the safe is abnormal, the usercan configure locations/areas in the home as being associated withspecific rules. For example, a rule can be set to begin video recordingonce a guest enters a specific space in the home. The locations in thehome can be marked by information learned from the monitored activitysuch as shown in FIG. 2, and/or can be identified using sensors and/orreceivers. The size, topology and/or blueprint of the home can bedetermined using triangulation techniques. In at least one embodiment,triangulation is used to determine the location of a guest.

In an embodiment, block 305 determines appropriate action based on theresults of block 304. The action based on block 304 can include actionrules set by default settings, configured by the user, and/or configuredby artificial intelligence. The appropriate actions can includenotifying a homeowner or other user of the wireless mesh network that anintrusion into the home has occurred. Notifications can include phoneapp notification, audio alarm, lighting alarm, an alarm, a notification,a text message, a phone call, an email, log, and/or turning on and/oroff connected devices. The action may include an initiation of a videoand/or audio recording using in home cameras when an intruder isdetected. For example, home cameras that are connected with the wirelessmesh network can be turned on or instructed to start recording by thesecurity system upon the determination to take appropriate action.Connected devices can include lights, heaters, air-conditioners, modems,routers, televisions, cameras, stoves, smart plugs, garage doors,electronic locks and/or other devices having wireless capabilities.

The action rules can include actions based on the determination ofabnormal and/or normal activities at block 304. The normal activitiesthat may result in an action can include the detection of a familymember entering a house. The action associated with the family memberentering the house can include turning the living room light on/off andturning on/adjusting the air conditioner. The adjusting air-conditionersand/or heaters can lower the electricity bill by triggering an offand/or low power setting when the house is empty.

The determined action can also assist as preemptive security measures.The preemptive security measures can include turning off the stoveand/or closing the garage door when no home activity is detected and thehome is determined to be empty.

In at least one embodiment, aggregate monitored activity can be used tocompare a profile and/or activity rules at block 304. For example, noactivity in the home for a set period of time can trigger an actionassociated with home being empty.

User set action rules can include notifications when a specific activityoccurs. A parent can set a rule while on vacation and a child is lefthome alone that triggers a notification action in response to thedetection of multiple visitors. Similarly, the user can configure therules to trigger a text message when an unknown device enters a home ata specified day and time.

In at least one embodiment, the user can configure the rules todetermine a threat level associated with the activity and providenotification according to the threat level. In one example, a user canconfigure the activity profile associated with a vacation and to notifythe user about all activity in the home during that time. The cleaningperson may enter the home while the user is on vacation and although theactivity profile associated with that specific Friday indicates the userbeing away, an alternative activity profile exists indicating that acleaning person is expected Friday morning. In this example, it can bedetermined that the threat level is low and may be set to trigger anemail to the user. Whereas an unknown device entering the home duringthe vacation period may trigger a high threat level which may trigger aphone call, text message and/or email to the user and/or the designatedreceiver. The threat level can be configured by the user, set bydefault, determined by an algorithm and/or artificial intelligence. Thethreat level determination can include comparing the set activityprofile to other activity profiles and historical data. Furthermore, thethreat level determination can be synchronized to incorporate acalendar, emails, social media information, and/or text messages.

In at least one embodiment, activity data determined to not require anaction can be erased, and/or stored in an external location for a setperiod of time. Similarly, historic activity data can be preserved for aset period of time, erased after a defined period of time, and/or sentto a remote location for storage.

In an embodiment, an administrative console, FIG. 4, allows the user toconfigure the settings. Configurable settings include manually editingand/or creating activity profiles 401, configuring user profiles 402,setting actions 403 associated with determinations based on themonitored activity 406 and/or rules 404, viewing and/or editing the hometopology 405 which can be used for recognized devices 407. Theadministrative console can be configured for accessibility only when thedevice is in the home network. In an embodiment, the administrativeconsole can be configured for accessibility from outside the homenetwork. Furthermore, the administrative console can be in the form of adedicated managing device, a smart home appliance, an app on a mobiledevice, an application from a computer, and/or a web based application.In at least one embodiment, the administrator can search for dates andtimes of when a device and/or a specific pattern was found in the home.

In at least one embodiment, a user can add devices to the rules. Devicescan include lights, heaters, air-conditioners, modems, routers,televisions, cameras, stoves, smart plugs, garage doors, electroniclocks, and/or other devices having wireless capabilities. Furthermore inan embodiment, the rules associated with devices can include an actionto operate the devices such as “dim the lights,” “raise the AC to 74degrees,” and/or “close garage door.” In an embodiment, the AP unit candrop the range of the Wi-Fi and/or Bluetooth when the home is determinedto be empty.

At least one embodiment allows the user to configure the known devicesand associate the devices with metadata, user profiles, rules and/oractivity profiles, Generic Attribute (GATT) profiles, MAC address, UUID,AID and/or other device information which known by the AP can be used toidentify devices.

The administrative console can include the functionality of the user toview the derived home topology. Based on the RSSI and/or RTDmeasurements, one or more AP devices can determine a home topology asseen in FIG. 2. In at least one embodiment, based on the RSSI and/or RTDmeasurements, one or more AP devices and one or more sensors candetermine a home topology.

The view activity setting, as seen in FIG. 4, can allow the user to tagactivities with identifying information. The activities setting canallow a user to identify activities in the home as associated withactivity profiles, user profiles, a category, a calendar entry, and/or asuspicious activity for further review.

In at least one embodiment, the user can turn on/off the intrusiondetection feature, set the feature for manual, and/or automaticconfiguration.

In at least one embodiment, the GATT profile can be used for intruderdetection. FIG. 5 shows the communication between GATT server 505 andclient 510. UIDS such as GATT MAC address, AID, and/or UUID (universallyunique identifier) can be used to detect devices in the home. The systemmay use the localization techniques available on Wi-Fi and Bluetooth forintruder detection if a device is known or the system can localize a newunknown device. The system can use long dedicated backhaul to do thecoordination between units as a detection mechanism for intruders. Thesystem can use Bluetooth packets that can be sent to a new device todetect an intruder. The system can use pre-association Wi-Fi forintruder detection and can combine all methods available forpre-association of Wi-Fi and for new Bluetooth devices to provide robustintruder detection. The system can use higher frequencies such as 28GHz, 60 GHz for more accurate radar type intruder detection.

The activity profiles can be constantly updated and become more accurateover time by tracking activity patterns and learning from them usinginformation available to the AP such as probing information, RSSI andRTD of Bluetooth, and/or Wi-Fi. The pattern can be related to, e.g.,RSSI, time of arrival, phase of arrival, etc. FIG. 6 shows an example ofa detected intruder device 605. Activities and activity patterns can beassociated with activity profiles.

FIG. 7 illustrates the use of Hidden Markov Model (HMM) to modelmovement of devices and/or masses. First order HMM may be used to modelmovement in and out of house or different locations. In at least oneembodiment, HMM is used for modeling movement. “Lt” of FIG. 7 candesignate the different part of a property and the “Ot” can be RSSI,time of arrival and/or round trip time.

The backhaul can be used for coordination and time measurements. FIG. 8shows a HMM model 805 for coordination and time measurements. Forexample, given a set of RSSI variation trends, V=fv1, v2, . . . , vM anda settled HMM, the hidden location sequence L=fl1, l2, . . . , l_N canbe estimated by employing the Viterbi algorithm.

In at least one embodiment, activity can include data packets betweenunits of the system that may be used to detect an intruder device movingbetween the units. FIG. 9 shows intruder devices 905 that are betweenthe units of the system. In an embodiment, the AP can allow an unknowndevice to connect to the AP unit and the data packets can be monitoredfor suspicious activity. The unknown device can also be monitored formovement between AP units.

Aspects of the disclosed embodiments may be described in terms ofalgorithms and symbolic representations of operations on data bitsstored in memory. For example, instructions for any of the techniquesdisclosed herein can be stored in non-transitory memory and a processoror other circuitry can execute the instructions to perform thetechniques. These algorithmic descriptions and symbolic representationsgenerally include a sequence of operations leading to a desired result.The operations require physical manipulations of physical quantities.Usually, though not necessarily, these quantities take the form ofelectric or magnetic signals that are capable of being stored,transferred, combined, compared, and otherwise manipulated. Customarily,and for convenience, these signals are referred to as bits, values,elements, symbols, characters, terms, numbers, or the like. These andsimilar terms are associated with physical quantities and are merelyconvenient labels applied to these quantities.

In some embodiments, the APs can include one or more radios. Forexample, different frequency bands can be used for communication signalsby using different radios.

While embodiments have been described in the context of fullyfunctioning computers, those skilled in the art will appreciate that thevarious embodiments are capable of being distributed as a programproduct in a variety of forms and that the disclosure applies equally,regardless of the particular type of machine or computer-readable mediaused to actually effect the embodiments.

While the disclosure has been described in terms of several embodiments,those skilled in the art will recognize that the disclosure is notlimited to the embodiments described herein and can be practiced withmodifications and alterations within the spirit and scope of theinvention. Those skilled in the art will also recognize improvements tothe embodiments of the present disclosure. All such improvements areconsidered within the scope of the concepts disclosed herein. Thus, thedescription is to be regarded as illustrative instead of limiting.

What is claimed is:
 1. A computer-implemented method for detectingphysical movement using a wireless mesh network, the wireless meshnetwork having a plurality of mesh points, each mesh point having awireless coverage, wherein the wireless mesh network includes afronthaul that provides wireless data communication to clients, and abackhaul that provides communication of management functions among theplurality of mesh points, the method comprising: retrieving a determinedboundary established through the plurality of mesh points; coordinating,via the backhaul, the mesh points to utilize one or more of the meshpoints to perform detection of a physical presence of devices within thedetermined boundary, wherein the detection is based on a transmissionand/or reception range difference in wireless network circuitry of theone or more mesh points that provides the wireless mesh network toestimate an existence of an anomalous device within the determinedboundary; and upon detecting a physical presence of the anomalous devicewithin the determined boundary, causing a security action to beperformed.
 2. The method of claim 1, further comprising: compiling adatabase of known devices based on monitoring unique identifiers (UIDs)of known devices that have previously conducted communication with thewireless mesh network.
 3. The method of claim 2, further comprising:compiling a pattern of the known devices including time of presence andestimated locations of the known devices within the wireless meshnetwork.
 4. The method of claim 1, further comprising: determining thata subject device is anomalous when data in a database of known devicesshow that an estimated location of the subject device deviates from apattern of known devices.
 5. The method of claim 1, further comprising:determining that a subject device is anomalous when historic data showthat a timing of a subject device's physical presence deviates from apattern of known devices.
 6. The method of claim 1, further comprising:determining that a subject device is anomalous when a subject device hasa UID not in a database of known devices.
 7. The method of claim 1,further comprising: determining that a subject device is anomalous basedon wireless communication prior to association between the wireless meshnetwork and the subject device.
 8. The method of claim 1, furthercomprising: determining that a subject device is anomalous based onsignal interference caused by the subject device or by the human body ofan intruder with physical possession of the subject device.
 9. Themethod of claim 1, further comprising: training one or more machinelearning models on known devices during an initial training period,wherein whether the physical presence of the subject device belongs toan anomaly is determined based on the one or more trained machinelearning models.
 10. The method of claim 1, further comprising:establishing the determined boundary based on utilizing the wirelessmesh network to detect an authorized user device.
 11. A network systemfor forming a wireless mesh network and detecting physical movementusing the wireless mesh network, the wireless mesh network having aplurality of mesh points, each mesh point having a wireless coverage,wherein the wireless mesh network includes a fronthaul that provideswireless data communication to clients, and a backhaul that providescommunication of management functions among the plurality of meshpoints, the network system having one or more processors configured toperform operations comprising: retrieving a determined boundaryestablished through the plurality of mesh points; coordinating, via thebackhaul, the mesh points to utilize one or more of the mesh points toperform detection of a physical presence of devices within thedetermined boundary, wherein the detection is based on a transmissionand/or reception range difference in wireless network circuitry of theone or more mesh points that provides the wireless mesh network toestimate an existence of an anomalous device within the determinedboundary; and upon detecting a physical presence of the anomalous devicewithin the determined boundary, causing a security action to beperformed.
 12. The system of claim 11, the operations furthercomprising: compiling a database of known devices based on monitoringunique identifiers (UIDs) of known devices that have previouslyconducted communication with the wireless mesh network.
 13. The systemof claim 12, the operations further comprising: compiling a pattern ofthe known devices including time of presence and estimated locations ofthe known devices within the wireless mesh network.
 14. The system ofclaim 11, the operations further comprising: determining that a subjectdevice is anomalous when data in a database of known devices show thatan estimated location of the subject device deviates from a pattern ofknown devices.
 15. The system of claim 11, the operations furthercomprising: determining that a subject device is anomalous when historicdata show that a timing of a subject device's physical presence deviatesfrom a pattern of known devices.
 16. The system of claim 11, theoperations further comprising: determining that a subject device isanomalous when a subject device has a UID not in a database of knowndevices.
 17. The system of claim 11, the operations further comprising:determining that a subject device is anomalous based on wirelesscommunication prior to association between the wireless mesh network andthe subject device.
 18. The system of claim 11, the operations furthercomprising: determining that a subject device is anomalous based onsignal interference caused by the subject device or by the human body ofan intruder with physical possession of the subject device.
 19. Thesystem of claim 11, the operations further comprising: training one ormore machine learning models on known devices during an initial trainingperiod, wherein whether the physical presence of the subject devicebelongs to an anomaly is determined based on the one or more trainedmachine learning models.
 20. The system of claim 11, the operationsfurther comprising: establishing the determined boundary based onutilizing the wireless mesh network to detect an authorized user device.21. A computer-implemented method for detecting physical movement usinga wireless mesh network, the wireless mesh network having a plurality ofmesh points, each mesh point having a wireless coverage, wherein thewireless mesh network includes a fronthaul that provides wireless datacommunication to clients, and a backhaul that provides communication ofmanagement functions among the plurality of mesh points, the methodcomprising: retrieving a determined boundary established through theplurality of mesh points; coordinating, via the backhaul, the meshpoints to utilize one or more of the mesh points to perform detection ofa physical presence of devices within the determined boundary, whereinthe detection is based on a difference in wireless network circuitry ofthe one or more mesh points that provides the wireless mesh network toestimate an existence of an anomalous device within the determinedboundary, and wherein the one or more mesh points are capable ofcommunicating using different types of communication technology; andupon detecting a physical presence of the anomalous device within thedetermined boundary, causing a security action to be performed.
 22. Themethod of claim 21, further comprising: compiling a database of knowndevices based on monitoring unique identifiers (UIDs) of known devicesthat have previously conducted communication with the wireless meshnetwork.
 23. The method of claim 22, further comprising: compiling apattern of the known devices including time of presence and estimatedlocations of the known devices within the wireless mesh network.
 24. Themethod of claim 21, further comprising: determining that a subjectdevice is anomalous when data in a database of known devices show thatan estimated location of the subject device deviates from a pattern ofknown devices.
 25. The method of claim 21, further comprising:determining that a subject device is anomalous when historic data showthat a timing of a subject device's physical presence deviates from apattern of known devices.
 26. The method of claim 21, furthercomprising: determining that a subject device is anomalous when asubject device has a UID not in a database of known devices.
 27. Themethod of claim 21, further comprising: determining that a subjectdevice is anomalous based on wireless communication prior to associationbetween the wireless mesh network and the subject device.
 28. The methodof claim 21, further comprising: determining that a subject device isanomalous based on signal interference caused by the subject device orby the human body of an intruder with physical possession of the subjectdevice.
 29. The method of claim 21, further comprising: training one ormore machine learning models on known devices during an initial trainingperiod, wherein whether the physical presence of the subject devicebelongs to an anomaly is determined based on the one or more trainedmachine learning models.
 30. The method of claim 21, further comprising:establishing the determined boundary based on utilizing the wirelessmesh network to detect an authorized user device.
 31. A network systemfor forming a wireless mesh network and detecting physical movementusing the wireless mesh network, the wireless mesh network having aplurality of mesh points, each mesh point having a wireless coverage,wherein the wireless mesh network includes a fronthaul that provideswireless data communication to clients, and a backhaul that providescommunication of management functions among the plurality of meshpoints, the network system having one or more processors configured toperform operations comprising: retrieving a determined boundaryestablished through the plurality of mesh points; coordinating, via thebackhaul, the mesh points to utilize one or more of the mesh points toperform detection of a physical presence of devices within thedetermined boundary, wherein the detection is based on a rangedifference in wireless network circuitry of the one or more mesh pointsthat provides the wireless mesh network to estimate an existence of ananomalous device within the determined boundary, and wherein the one ormore mesh points are capable of communicating using different types ofcommunication technology; and upon detecting a physical presence of theanomalous device within the determined boundary, causing a securityaction to be performed.
 32. The system of claim 31, the operationsfurther comprising: compiling a database of known devices based onmonitoring unique identifiers (UIDs) of known devices that havepreviously conducted communication with the wireless mesh network. 33.The system of claim 32, the operations further comprising: compiling apattern of the known devices including time of presence and estimatedlocations of the known devices within the wireless mesh network.
 34. Thesystem of claim 31, the operations further comprising: determining thata subject device is anomalous when data in a database of known devicesshow that an estimated location of the subject device deviates from apattern of known devices.
 35. The system of claim 31, the operationsfurther comprising: determining that a subject device is anomalous whenhistoric data show that a timing of a subject device's physical presencedeviates from a pattern of known devices.
 36. The system of claim 31,the operations further comprising: determining that a subject device isanomalous when a subject device has a UID not in a database of knowndevices.
 37. The system of claim 31, the operations further comprising:determining that a subject device is anomalous based on wirelesscommunication prior to association between the wireless mesh network andthe subject device.
 38. The system of claim 31, the operations furthercomprising: determining that a subject device is anomalous based onsignal interference caused by the subject device or by the human body ofan intruder with physical possession of the subject device.
 39. Thesystem of claim 31, the operations further comprising: training one ormore machine learning models on known devices during an initial trainingperiod, wherein whether the physical presence of the subject devicebelongs to an anomaly is determined based on the one or more trainedmachine learning models.
 40. The system of claim 31, the operationsfurther comprising: establishing the determined boundary based onutilizing the wireless mesh network to detect an authorized user device.